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SANDIA REPORT

SAND2016-0232 Unclassified Unlimited Release Printed January 2016

Fukushima Daiichi Unit 1 Accident Progression Uncertainty Analysis and Implications for Decommissioning of Fukushima Reactors – Volume I

Prepared by

Severe Accident Analysis Department - 06232

Sandia National Laboratories

Albuquerque, New Mexico 87185 and Livermore, California 94550

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Approved for public release; further dissemination unlimited.

ii

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SAND2016-0232

Unclassified Unlimited Release

Printed January 2016

Fukushima Daiichi Unit 1 Accident Progression Uncertainty Analysis and Implications for Decommissioning of Fukushima Reactors – Volume I

Severe Accident Analysis Department – 06232

Sandia National Laboratories

P.O. Box 5800

Albuquerque, New Mexico 87185-MS0748

Abstract

Sandia National Laboratories (SNL) has conducted an uncertainty analysis (UA) on

the Fukushima Daiichi unit (1F1) accident progression with the MELCOR code. The

model used was developed for a previous accident reconstruction investigation jointly

sponsored by the US Department of Energy (DOE) and Nuclear Regulatory

Commission (NRC). That study focused on reconstructing the accident progressions,

as postulated by the limited plant data. This work was focused evaluation of

uncertainty in core damage progression behavior and its effect on key figures-of-

merit (e.g., hydrogen production, reactor damage state, fraction of intact fuel, vessel

lower head failure). The primary intent of this study was to characterize the range of

predicted damage states in the 1F1 reactor considering state of knowledge

uncertainties associated with MELCOR modeling of core damage progression and to

generate information that may be useful in informing the decommissioning activities

that will be employed to defuel the damaged reactors at the Fukushima Daiichi

Nuclear Power Plant. Additionally, core damage progression variability inherent in

MELCOR modeling numerics is investigated.

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ACKNOWLEDGEMENTS

The authors would like to acknowledge the U.S. Department of Energy’s

Office of Nuclear Energy for providing the funding which supported this

work.

The editors would like to thank Donald Kalinich for performing the

MELCOR analyses described in this report and would also like to

acknowledge Jeff Cardoni for supplying decay heat SCALE calculations

and Mathew Denman for his contributions to the development of the

uncertainty framework that was implemented for this analysis.

This report was edited for publication by Randall Gauntt and Patrick

Mattie.

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CONTENTS

1 Introduction 1

1.1 Background .................................................................................................................... 1

1.2 Purpose ........................................................................................................................... 2

1.3 Document Outline .......................................................................................................... 3

2 MELCOR Model Description 5

2.1 SNL BWR/4 Mk-I MELCOR Model History ............................................................... 5

2.2 MELCOR Computer Code Description ......................................................................... 6

2.3 1F1 MELCOR Model Nodalization............................................................................... 7

2.4 1F1 Radionuclide Inventory and Decay Heat Characterization .................................. 12

2.5 Accident Event Timeline ............................................................................................. 13

2.6 Computational Platform and Code Version ................................................................. 16

3 MELCOR Analysis 17

3.1 Uncertain Modeling Parameters .................................................................................. 17

3.2 UA Execution............................................................................................................... 21

4 Results of MELCOR Uncertainty Analysis 23

4.1 Overview and General Observations ........................................................................... 23

4.1.1 Primary System Pressure Response ............................................................... 23

4.1.2 Core Water Level Boildown .......................................................................... 24

4.1.3 Core Degradation and Relocation Events ...................................................... 26

4.1.4 Hydrogen Generation ..................................................................................... 26

4.1.5 Lower Head Failure and Core Material Relocation to Reactor Cavity .......... 29

4.1.6 Containment Pressure Response .................................................................... 29

4.2 Observations and Areas of Future Improvement ......................................................... 31

5 Implications for Fukushima Decommissioning 33

5.1 Relocation of Control Blades and Fuel Bearing Materials to Lower Vessel Head ..... 33

5.2 Relocation of Lower Head Materials into Reactor Cavity........................................... 35

5.3 Conclusions of Material Relocation Analysis.............................................................. 36

5.4 Hydrogen Generation ................................................................................................... 37

6 A Quantification of code numerical sensitivity with imlications on precision of

computed results 39

7 Summary and Conclusions 45

8 References 47

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FIGURES

Figure 2.1 – Illustration of the Range of Phenomena and Processes Modeled in the MELCOR

Accident Analysis Code……………………………………………………………………… 7

Figure 2.2 – Reactor Pressure Vessel Nodalization……………………………………….......... 10

Figure 2.3 – Core Nodalization showing control volume and fuel/component discretization used

to characterize the reactor core……………………………………………………………... 11

Figure 2.4 – Containment Nodalization showing drywell and wetwell compartments………… 12

Figure 2.5 – Normalized feedwater coast-down curve…………………………………………. 15

Figure 3.1 – Fukushima 1F1 Decay Heat Time History Curves………………………………... 20

Figure 3.2 – Statistical Representation of Shark-Fin Failure Curves Overlaid on the SOARCA

Time at Temperature Failure Curve………………………………………………………… 20

Figure 3.3 – Diagram of Information Flow of the 1F1 Uncertainty Analysis………………….. 21

Figure 4.1. Primary system pressure showing range of predicted main steam failure times…... 24

Figure 4.2. Reactor Vessel water level………………………………………………………… 25

Figure 4.3. Fraction of intact control rod mass in the core…………………………………….. 27

Figure 4.4. Fraction of intact fuel mass in the core……………………………………………. 28

Figure 4.5. Mass of hydrogen produced in-vessel……………………………………………... 28

Figure 4.6. Mass of core material ejected to the reactor cavity………………………………... 29

Figure 4.7. Containment drywell pressure response. ………………………………………….. 30

Figure 4.8. Containment wetwell pressure response ………………………………………….. 31

Figure 5.1. Fraction of Intact Control Blade Mass in Core Region…………………………… 34

Figure 5.2. Fraction of Intact Fuel Mass in Core Region……………………………………… 35

Figure 5.3. Mass of Core Materials Ejected to Reactor Cavity………………………………... 36

Figure 5.4. Hydrogen Generation Results shown in CDF format……………………………… 37

Figure 6.1. Base Case Comparison with Small Perturbation analysis (RPV pressure)………... 41

Figure 6.2. Base Case Comparison with Small perturbation analysis (Drywell Pressure)…….. 41

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Figure 6.3. Base Case Comparison with Small Perturbation analysis (RPV Water Level)……. 41

Figure 6.4. Base Case Comparison with Small perturbation analysis Intact Control Rod

Mass)………………………………………………………………………………………... 42

Figure 6.5. Base Case Comparison with Small perturbation analysis Intact Fuel Mass)……… 42

Figure 6.6. Base Case Comparison with Small perturbation analysis (In-Vessel Hydrogen)…. 42

Figure 6.7. Base Case Comparison with Small perturbation analysis (Mass of Water in the

Lower Plenum)……………………………………………………………………………... 43

Figure 6.8. Base Case Comparison with Small perturbation analysis (Mass of Material

Ejected)……………………………………………………………………………………... 43

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TABLES

Table 2.1 – 1F1 Event Timeline……………………………………………………...………… 14

Table 2.2 – IC Utility Factor……………………………………………………………….. …... 16

Table 3.1 – State of Knowledge Uncertain Parameters……………………………………….... 18

Table 6.1 – Small Change Distributions Based on Rlz13 Sampled Values…………………...... 40

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ACRONYMS

1F1 Fukushima Daiichi Unit 1



ADS automatic depressurization system

BAF bottom of active fuel

BSAF Benchmark Study of the Accident at the Fukushima Daiichi Nuclear Power Station

(BSAF) Project

BWR boiling water reactor

CDF Cumulative Distribution Function

CRDHS control rod drive hydraulic system

CRGT control rod guide tube

CST condensate storage tank

CV control volume

CVH Control Volume Hydrodynamics (package)

DCH Decay Heat

DOE Department of Energy

DW drywell

FoM Figures of Merit

HPCI high-pressure coolant injection system

IC isolation condenser

JST Japanese Standard time

LPCS low-pressure core sprays

MCCI molten core concrete interaction

MELCOR not an acronym

MOX mixed oxide

MSIV main steam-line isolation valve

MSL main steam line

NRC Nuclear Regulatory Commission

RCIC reactor core isolation cooling system

RCS reactor coolant system

RHR residual heat removal system

RN Radionuclide (package)

RPV reactor pressure vessel

x

SBO station blackout

SCALE6 Standardized Computer Analyses for Licensing Evaluation

SNL Sandia National Laboratories

SOARCA State of the Art Reactor Consequence Analysis

SRV safety relief valve

TAF top of active fuel

TaT Effective Fuel Failure Temperature (time-at-temperature)

TEPCO Tokyo Electric Power Company

WW wetwell

1

1 INTRODUCTION

1.1 Background Japan suffered an immense tragedy as a result of the 2011 Tohuku earthquake and resulting

tsunami that caused widespread damage to the infrastructure of the country and more than

20,000 deaths, most from the tsunami. The magnitude 9 (Mw) earthquake, centered roughly

20 miles deep and 43 miles east of Japan, took place at 14:46 (JST) on March 11, 2011, and

produced catastrophic damage to buildings, roads, communications, and regional electrical

power. Damage to the Sendai region was especially large owing to its proximity to the epicenter

of the seismic event that produced a tsunami with an estimated height that exceeded 14 meters at

the site of the Fukushima reactors. The earthquake at 14:46 JST resulted in a scram and a

regional loss of electrical power, requiring the Fukushima Daiichi power plants (1F1, 1F2, and

1F3) to start emergency on-site diesel powered generators to maintain cooling at the plants. 1F4

was defueled at the time for maintenance, and 1F5 and 1F6 were in a state of cold shutdown for

maintenance.

The first of several tsunamis produced by the earthquake reached the Fukushima Daiichi site at

roughly 15:27. At 15:46, a wave exceeding 14 meters flooded buildings resulting in the loss of

emergency diesel powered AC generators and producing conditions known as Station Blackout

(SBO). DC power was also lost at 1F1 and 1F2. The plants were effectively isolated from the

ultimate heat sink (the ocean) due to the loss of the seawater pumps that were flooded and

destroyed, and the emergency cooling systems eventually failed; each of the three units

subsequently suffered core damage of varying degrees as a result of loss of water level in the

reactor cores. Significant hydrogen generation resulted from the oxidation of the exposed

Zircaloy fuel cladding, which occurs at elevated temperatures; this can generate heat greater than

the decay heat in the fuel, and therefore may have accelerated the release of fission products

from the damaged fuel to the containment vessels. With no way to reject the decay heat from the

reactors, the suppression pools eventually became thermally saturated. This produced pressures

in the containment vessels that eventually exceeded their design pressures. Containment venting

was attempted. However, due to difficulties in accessing and operating vent valves, venting was

either unsuccessful or delayed.

Ultimately, the containment systems leaked, failed, or were intentionally vented, resulting in the

release of radioactivity to the reactor buildings and the environment. Combustible gasses

produced from the core damage, and potentially from molten core–concrete interaction,

accumulated in the 1F1 and 1F3 reactor buildings, causing explosions and destruction of portions

of the buildings [2].

In response to the accident at the Fukushima Daiichi nuclear power station in Japan, the U.S.

Nuclear Regulatory Commission (NRC) and Department of Energy agreed to jointly sponsor an

accident reconstruction study [2] as a means of assessing severe accident modeling capability of

the MELCOR code [1]. As part of that study, Sandia National Laboratories (SNL) developed

MELCOR 2.1 models of the Fukushima 1F1, 1F2 and 1F3 reactors. The MELCOR models used

in that study were based on the State of the Art Reactor Consequence Analysis (SOARCA)

Peach Bottom BWR/4 Mk-I MELCOR model [3]. The Peach Bottom plant is a larger but quite

similar in reactor design to the reactors at Fukushima. The containments are the General Electric

Mk-I design where a torus-shaped suppression pool serves to prevent steam over-pressurization

of the containment. The Peach Bottom MELCOR model was used as a template for developing

2

the Fukushima MELCOR models. Modifications were made to the Fukushima MELCOR models

to include important differences in the Fukushima reactors, such as reactor dimensions, number

of fuel assemblies, and operating power.

1.2 Purpose The primary purpose of this work was to evaluate the influence of in-core damage progression

input parameters on selected key figures-of-merit (FoMs) (e.g., fraction of failed fuel, time of

lower head failure, total hydrogen produced in-core, etc.). The impetus for this is to develop a

better understanding of potential plant damage states (i.e., degree of core degradation, locations

of intact and degraded materials, etc.) which can in turn be used to help inform future

decommissioning efforts and define areas where additional information obtained from

decommissioning activities could improve understanding of severe accident progression.

In addition, in the previous 1F1 analysis [2] deviations were seen between some of the analysis

results and the Tokyo Electric Power Company (TEPCO) plant data. For example, the 1F1 model

predicted lower head failure between 14 and 15 hr, while the plant data pressure rise that can be

postulated as indicative of lower head failure is between 11 and 12 hr. This deviation is not

unexpected, as many of the parameters used in the MELCOR models that influence core

degradation, and by extension, lower head failure have been developed based on a small number

of separate effects tests and expert opinion; they have not been quantified with full-scale integral

tests at representative boiling water reactor (BWR) station blackout (SBO) conditions.

The traditional method to perform this analysis would be to execute a series of sensitivity

analyses on a set of parameters that were deemed to be important to core damage progression.

While this method can potentially find a set of parameters that result in a better prediction with

respect to the data, its ad hoc nature makes it difficult to quantify the sensitivity of the results to

the input parameters. Given the availability of servers with hundreds of central processing unit

(CPU) cores, using an uncertainty analysis approach (i.e., identifying key input parameters,

accounting for uncertainty in those parameters, creating as statistically sufficient number of

samples from the uncertainty distributions) to address this type of problem is much more

tractable, traceable, and provides outputs that are amenable to statistical analysis.

Recent uncertainty analysis (UA) reactor analyses performed by SNL have looked at a wide

range of parameters with the intent of addressing (at least to some extent) uncertainty in all

phases of the accident sequence [4],[5]. For this analysis a focused set of uncertain parameters

was used, specifically those associated with in-core damage progression [22]. This was done as

the figures-of-merit (FoMs) of interest are directly related to in-core damage progression phase

of the accident sequence.

An additional outcome of this work was the characterization of code sensitivity to the varied

model inputs as well as inherent variation due to the numerical methods used in MELCOR to

predict severe accident progression. The additional variability in predicted results driven by the

code’s inherent precision was evaluated by performing an ensemble of analyses similar to the

uncertainty variations but where the range of uncertain model inputs was restricted to a relatively

small span (+/- 1%). This was done to quantify the order of magnitude of the numerical precision

that can be expected of complex multi-effects codes, in this case MELCOR, in the midst of

broader state of knowledge variations employed in the uncertainty analysis. This characterization

provides the code user with insights when assessing the magnitude of variance in the results

3

relative to the fundamental code precision, an important consideration when evaluating the

statistical importance of the selected uncertain parameters and resulting range of potential

damage states.

1.3 Document Outline A summary description of the 1F1 MELCOR model is provided in Section 2. The UA approach,

the characterization of the uncertain input parameter distributions, and a description of the

analyses performed with the 1F1 MELCOR model are discussed in Section 3. Results from the

analyses are presented in Section 4. Section 5 addresses the relevance of these uncertainty results

to anticipated future decommissioning activates for the Fukushima Daiichi reactors. In Section 6

the results of a code numerical precision study are described to aid in assessing code results.

Finally, summary and conclusions are presented in Section 7.

5

2 MELCOR MODEL DESCRIPTION

The 1F1 MELCOR model was originally developed from the MELCOR BWR/4 Mk-I boiling

water reactor model created for the NRC’s SOARCA project [3]. Care was taken during 1F1

model development to ensure that it adequately represented a BWR/3 reactor, which is a smaller

reactor compared to the BWR/4 designs of 1F2, 1F3, and Peach Bottom. The SOARCA

documentation [8] provides a summary description of that model. A comprehensive description

of that model is also available in reference [9]. Section 3 of the Fukushima Daiichi accident

study [2] contains the majority of the SOARCA model summary description along with

subsections describing the implementation of Fukushima-specific information used to create the

Fukushima MELCOR model.

Since the Fukushima Daiichi accident study report [2] was issued, new information on the plant

geometry and the accident sequence has been provided by the Japan Atomic Energy Agency

(JAEA) [10] to SNL. This new information has been incorporated into the 1F1 model that is used

for this analysis.

This section will provide a brief summary of the model descriptions given in references [8] and

[9], with a focus on model nodalization. In addition, an accident event timeline will be provided,

along with discussions of how the timeline events were implemented into the 1F1 MELCOR

model.

A review of the historical development of the BWR/4 Mk-I MELCOR model is provided in

Section 2.1. A brief description of the MELCOR code is provided in Section 2.2. Section 2.3

contains a summary description of the 1F1 MELCOR model nodalization. Section 2.4 provides a

discussion of the 1F1 radionuclide and decay heat characterization. A discussion of the accident

event timelines and associated model modifications are given in Section 2.5. Finally, Section 2.6

provides the computational platform and code executables used for this work.

2.1 SNL BWR/4 Mk-I MELCOR Model History The MELCOR SOARCA BWR/4 Mk-I model was originally created at Brookhaven National

Laboratory for code assessment applications with MELCOR 1.8.0; it is based on the Peach

Bottom Atomic Power Station Unit 2 Reactor design. The model was subsequently used by the

Oak Ridge National Laboratory to study differences between fission product source terms

predicted by MELCOR 1.8.1 and those generated for use in NUREG-1150 [18], “Severe

Accident Risks: An Assessment for Five U.S. Nuclear Power Plants”, using the Source Term

Code Package [11]. In 2001, SNL refined the BWR/4 core nodalization to support the

developmental assessment and release of MELCOR 1.8.5. These refinements concentrated on the

spatial nodalization of the reactor core (in terms of fuel and structural material and

hydrodynamic volumes) used to calculate in-vessel melt progression. However, the overall scope

of the model also expanded to permit a wider spectrum of accident scenarios to be examined,

some of which involved operation or delayed failures of plant safety systems.

These developments culminated in a model that was applied in the reassessment of radiological

source terms for high burnup and mixed oxide (MOX) core designs, and a comparison of their

release characteristics [12] to the regulatory prescription outlined in NUREG-1465, “Accident

Source Term for Light-Water Nuclear Power Plants,” issued February 1995 [13]. These

6

calculations addressed a wide spectrum of postulated accident sequences, which required the

following new models to represent diverse plant design features:

modifications of features needed to achieve steady-state reactor conditions (e.g.,

recirculation loops, jet pumps, steam separators, steam dryers, feedwater flow, control

rod drive hydraulic system (CRDHS), main steam lines, turbine/hotwell, core power

profile),

new models and control logic to represent coolant injection systems (e.g., RCIC,

HPCI, residual heat removal system (RHR), and low-pressure core sprays (LPCS))

and supporting water resources (e.g., condensate storage tank (CST) with

switchover), and

new models to simulate reactor vessel pressure management (e.g., safety relief valves

(SRVs), safety valves, automatic depressurization system (ADS), and logic for

manual actions to affect a controlled depressurization if torus water temperatures

exceed the heat capacity temperature limit).

Subsequent work in support of other NRC research programs motivated further refinement and

expansion of the Peach Bottom MELCOR model in two broad areas. The first area focused on

the spatial representation of primary and secondary containment. The drywell portion of primary

containment has been subdivided to distinguish thermodynamic conditions internal to the

pedestal from those within the drywell itself. Refinements have also been made to the spatial

representation and flow paths within the reactor building (i.e., secondary containment). The

second area has focused on bringing the model up to current best practice standards for

MELCOR 1.8.6 [14].

The SOARCA BWR/4 Mk-I MELCOR model was chosen as the starting point for the

development of the Fukushima reactor models, as it is the current state-of-the-art BWR/4 Mk-I

MELCOR model. At Fukushima Daiichi, Units 2 and 3 are BWR/4 reactors, while Unit 1 is of a

very similar reactor design (BWR/3). Furthermore, Units 1, 2, and 3 all have the Mk-I

containment design.

2.2 MELCOR Computer Code Description MELCOR [1] is a fully integrated, engineering-level computer code that models the progression

of severe accidents in light-water reactor nuclear power plants. MELCOR is being developed at

Sandia National Laboratories for the U.S. NRC as a second-generation plant risk assessment tool

and the successor to the Source Term Code Package [11]. A broad spectrum of severe accident

phenomena in both boiling and pressurized water reactors is treated in MELCOR in a unified

framework. These include thermal-hydraulic response in the reactor coolant system (RCS),

reactor cavity, containment, and confinement buildings; core heatup, degradation, and relocation;

core-concrete attack; hydrogen production, transport, and combustion; fission product release;

and transport behavior. Figure 2.1 illustrates the wide range of processes and phenomena during

a severe accident that are modeled in the MELCOR code.

Current uses of MELCOR include estimation of severe accident source terms and their

sensitivities and uncertainties in a variety of applications.

7

Figure 2.1 – Illustration of the Range of Phenomena and Processes Modeled in the MELCOR Accident Analysis Code.

2.3 1F1 MELCOR Model Nodalization The RPV nodalization in the 1F1 model is similar to that in the Peach Bottom SOARCA model

[9] and the 1F1 model in [2]. Changes were made to the nodalization to make it consistent with

the RPV nodalization in the JAEA data set [10]. This alignment simplifies the processing of the

JAEA data into MELCOR input (e.g., control volume elevations, control volume free volumes,

etc.).

Excluding the core region, the reactor pressure vessel is broken up into the following control

volumes (CVs) (see Figure 2.2). Note that while control volumes define the spatial domain of the

MELCOR analysis, the control volumes are connected with MELCOR “Flow Paths” which

define the flow network that allows the flow and exchange of hydrodynamic materials such as

water, steam and hydrogen:

lower plenum (CV_320)

jet pumps (CV_300)

lower downcomer (CV_312)

8

upper downcomer (CV_314)

upper core shroud (CV_345)

steam separators (CV_350)

steam dome (CV_360)

Flowpaths (FL) connect the RPV CVs:

steam dome-to-upper downcomer (FL_360-314)

upper downcomer-to-lower downcomer (FL_314-312)

lower downcomer-to-jet pumps (FL_312-300)

jet pumps-to-lower plenum (FL_300-320)

upper core shroud-to-steam separators (FL_345-350)

steam-separators-to-steam dome (FL_350-360)

steam-separators-to-upper downcomer (FL_350-314)

Also, not shown, are the FLs that connect the lower plenum to the channel and bypass CVs at the

bottom of the core (one FL per homogenized region of the core {COR ring}) and that connect

the channel and bypass CVs at the top of the core to the upper core shroud CV (one FL per COR

ring).

The heat structures (HS) (shown in various shades of orange in Figure 2.2) that represent the

RPV vessel are

hemispherical portion of steam dome CV (HS_360-200.2)

cylindrical portion of steam dome CV (HS_360-200.1)

cylindrical portion of upper downcomer CV (HS_314-200)

cylindrical portion of lower downcomer CV (HS_312-200)

The heat structures below the core shroud are

core shroud support structures (HS_320-320)

lower downcomer/lower plenum wall (HS_320-312)

The core shroud heat structures are

one HS at each core axial level from level 5 to level 17 (HS_xxx-yyy_zz), where xxx is

the CV# of the CV inside the core shroud, yyy is the CV# of the CV outside the core

shroud, and zz is an the index number that differentiates HS for different core axial levels

that are associated the same inside and outside CVs.

The upper core shroud is represented by two heat structures

cylindrical portion of the upper core shroud (HS_345-312.1)

domed portion of the upper core shroud (HS_345-312.2)

The steam separators are represented by three heat structures

9

the standpipes (HS_350-314.1)

the portion of the separators within the upper downcomer CV (HS_350-314.2)

the portion of the separators within the steam dome CV (HS_350-360)

Note that the lower head is not treated as a heat structure, rather it modeled in MELCOR as part

of the COR package.

In addition, there are CVs, FLs, and HSs that represent the four main steam lines and the two

recirculation pump loops (not shown in Figure 2.2). Note that Heat Structures (HS), another

MELCOR input component define physical elements of the reactor and containment such as the

mass of steel in RCS piping or the walls of the containment and reactor building. Heat structures

can absorb (store) or transfer heat by conduction, convection or radiation process considered in

the MELCOR time-step advancement.

Figure 2.3 shows the both the COR and CV/FL nodalization of the core. In the COR package, the

core is modeled with 6 rings and 17 axial core levels. The axial levels are comprised

level 1: control rod stub tubes (CRSTs)

level 2-5: control rod guide tubes (CRGTs)

level 6: lower core plate, lower tie, plate, nose pieces, and “Elephant’s Foot”

level 7-16: fuel, control blades, and canisters

level 17: top guide and upper tie plate

Note that while there are ring 6 COR cells in axial levels 2-5, there are no core materials in those

cells.

The COR cells in axial levels 1-6 are associated with the lower plenum (CV_320). In core region

above the lower core plate/lower plenum there are 5 channel and 5 bypass CVs (arrayed axial) in

each ring. Two axial COR cells are associated with a corresponding CV (as shown in Figure

2.3), with the exception of the top-most CVs which are associated with 3 COR cells.

10

Figure 2.2 – Reactor Pressure Vessel Nodalization

11

Figure 2.3 – Core Nodalization showing control volume and fuel/component discretization used to characterize the reactor core.

12

The primary containment of the Mk-I design consists of two separate regions: a drywell (DW)

and a wetwell (WW) (see Figure 2.4). The drywell is divided into two CVs, one for the region

within the pedestal under the RPV (CV205) and one for the balance of the drywell (CV200). The

wetwell is also divided into two CVs, one for the drywell steam vents that connect the drywell to

the torus (CV210), and one for the torus itself (CV220). Previous analyses have used multiple

CVs for the torus, both in terms of dividing it into separate atmosphere and pool CVs, as well as

dividing it into multiple radial segments. These multiple-CV implementations are done as an

attempt to model the thermal gradients that occur due to a single SRV venting into one region of

the torus. The concept of modeling the thermal stratification thought to occur in the suppression

pool is a topic of current intense study and when this phenomena becomes better understood, the

effects will be included in equivalent MELCOR nodalization that would capture this effect,

informed by new experimental data or other detailed code analyses (CFD for example). For now,

we neglect this effect in the current analyses.

Figure 2.4 – Containment Nodalization showing drywell and wetwell compartments.

2.4 1F1 Radionuclide Inventory and Decay Heat Characterization The SNL MELCOR model for 1F1 implements consistent lumped radionuclide (RN) class

inventories and class powers, total core decay power, and nuclide-level inventories (for MACCS)

that are generated from the SCALE6 code package [19]. The pertinent MELCOR and MACCS

input records are derived directly from SCALE6 burnup calculations in a fully automated

fashion. In particular, ORIGEN-S is used in conjunction with the Automatic Rapid Processing

(ARP) module for problem-dependent cross sections. ARP estimates problem-dependent cross

13

sections by interpolating between data libraries available at discrete levels of enrichment,

burnup, and void fraction. ORIGEN-S/ARP outputs are processed automatically by Perl scripts

to directly create MELCOR inputs of RN class inventories, class specific decay powers, and total

core decay power. Concurrently, MELMACCS inputs for nuclide inventories are written to

enable MACCS to breakdown the MELCOR source term into isotopic releases. The isotopic

inventories are entirely consistent with the lumped RN inventories specified in MELCOR, since

all of the information is derived from the same ORIGEN-S calculations. A comprehensive

description of the ORIGEN-S/ARP analyses of Fukushima and the post-processing methods used

to create MELCOR/MACCS inputs is provided by [15].

The current MELCOR inputs are derived from calculations that make use of the pre-generated,

collapsed data libraries available in SCALE6 that are based on TRITON [19] calculations with

ENDF-B/V cross sections; these cross section libraries are used in conjunction with decay data

from ENDF-B/VII.1 for the calculation of the MELCOR and MACCS inputs. The surrogate

BWR cross section libraries in SCALE6 are reasonably representative of the Fukushima reactors

for the purposes of lumped RN class inventories and decay heating, and are more than adequate

for uncertainty analyses that concentrate on severe accident phenomena such as core

degradation. However, in anticipation of future source term analyses, SNL has recently

completed TRITON modeling efforts to generate data libraries for each of the fuel assembly

types in units 1-3 at Fukushima. These new libraries make use of the latest ENDF-B/VII.1 cross

sections and are more representative of the Fukushima fuel than the pre-generated ENDF-B/V

libraries. Accurate data libraries are necessary for best-estimate inventories in source term

analyses, particularly for certain actinides (238

Pu, 241

Pu) and neutron absorption products (134

Cs)

that are important for health effects. Fukushima research on source term quantification will

utilize updated SCALE6 calculations that make use of the detailed fuel assembly models and

data libraries.

The ORIGEN-S/ARP models use plant-specific core design and operating history information

from TEPCO [20]. This includes power and burnup distributions, the number of previous

irradiation and decay cycles, enrichments, void fraction distributions, overall thermal power and

fuel loading, and the time of shutdown in the last operating cycle. The types of fuel assemblies,

such as the details of fuel lattice (i.e., 8x8, 9x9, etc.) and water rod size/placement, are treated by

the ARP libraries that are generated by TRITON.

2.5 Accident Event Timeline Table 2.1 lists the key events in the 1F1 accident sequence [2]. For this analysis the model is

only run to the time at which fresh water injection was started (15 hr) as results beyond this time

are not relevant for the purpose of this report.

Information provided in [17] indicates that the main steam isolation valves did not start to close

until 52.5 s, and were not fully closed until 55.5 s. These values, with a linear closure rate, were

implemented into the main steam isolation valve (MSIV) control logic in the 1F1 model.

Reference [17] also provided a feedwater flow rate for the time from scram and when the

feedwater pump halted. This feedwater flow rate time-history was implemented into the

feedwater flow rate control logic in the 1F1 model (see Figure 2.5).

14

Table 2.1 – 1F1 Event Timeline

Date and time Time after scram (hr) Event

3/11 14:46 0.00 Earthquake; reactor scrammed; feedwater pump

coast-down

3/11 14:47 0.014583 to 0.0154167

(52.5 to 55.5 s) MSIVs close

3/11 14:52 0.10 Isolation Condensers (ICs) automatically starts

(Train A and B)

3/11 15:03 0.28 Isolation Condensers (Train A and B) manually

stopped to control cool down rate

3/11 15:17 0.52 Isolation condenser train A manually started

3/11 15:19 0.55 Isolation condenser train A manually stopped



3/11 15:27 0.68 First tsunami wave hits



3/11 15:35 0.82 Second tsunami wave hits

3/11 15:41 0.92 Station Blackout


(not implemented in the model)



3/11 21:30 6.73 Isolation Condenser train A manually started


3/12 5:46 15.00 Fresh water injection from fire water pump starts

End of simulation

15

Figure 2.5 – Normalized feedwater coast-down curve

The 1F1 reactor is equipped with isolation condensers (IC’s). These isolation condensers reside

in the upper reactor building and consist of a shell and tube heat exchanger that condenses

primary system steam, rejecting heat to the shell side of the condenser. Motor operated valves

must be manually opened in order for steam to flow to the condenser and for condensed water to

return to the RPV. Each IC has a rated capacity of 42.2 MW [17]. The capacity is calculated by

multiplying the rated capacity by a pressure-based utility factor [16] (see Table 2.2) to account

for the decrease in IC efficiency at lower pressures. In the model, a simple negative energy

source function (equal to the 42.2 MW times the utility factor times the number of ICs operating)

is used to simulate the operation of the ICs.

16

Table 2.2 – IC Utility Factor

Pressure (Pa) utility factor (-)

3.26E+06 0.12

4.13E+06 0.33

5.07E+06 0.55

6.15E+06 0.80

7.24E+06 1.00

Also, the IC operation as specified by the 1F1 timeline was modified. For the last two operating

periods (3.53 – 3.65 hr; 6.73 – 20.23 hr), IC operations were not included in the analysis due to

the brevity of the first operation period and the model prediction of RPV depressurization prior

to the last operation of the IC. Furthermore, the presence of non-condensable gases and aerosols

(as predicted by the MELCOR model) is assumed to largely disable the ICs’ functionality. This

treatment is consistent with the current OECD-NEA BSAF 1F1 characterization of the IC

operation [17].

2.6 Computational Platform and Code Version The MELCOR models were executed on an SNL Windows server cluster:

DEF Cluster, 66 nodes, total of 264 cores

Hyperthreading NOT enabled

Dell PowerEdge 2950 server (dual processors)

OS: Windows Enterprise Server 2003

Xeon 5160, 3.0 GHz 2-core (no turbo), 4 MB L2 cache, 80 W max TDP

8GB RAM/System

All of the MELCOR analyses were run with MELCOR 2.1 version 5864 using the following

executables:

Melgen_RL_NL_5864.exe

Melcor_RL_NL_5864.exe

17

3 MELCOR ANALYSIS

This section discusses the 1F1 MELCOR cases that were run for the UA. A summary of the

uncertain parameters used in the analysis is given in Section 3.1. The methodology used to

execute the UA cases is Section 3.2.

3.1 Uncertain Modeling Parameters A scoping study was performed to make a preliminary selection of uncertain parameters for this

UA [22]. Based on that work, a final set of uncertain parameters (see Table 3.1) were selected

and characterized (see Section 2 of [23]) for use in this analysis. The selection of uncertain

model parameters for the most part were chosen based on prior experience in previously

conducted uncertainty quantification studies (eg. SOARCA Peach Bottom – reference 4 and 5)

where the focus was on parameters thought to affect core melt progression and hydrogen

generation. SNL improved on the technical justifications for these parameters, ranges and

distributions [24]. While the details on these justifications and distributions will not be repeated

here, the selected parameters are known to affect timing of cladding failure, fuel rod failure,

extent of hydrogen generation, melt relocation in core (radial and lateral spreading) and re-

freezing behavior of draining molten core materials. These are summarized in Table 3.1.

In addition to model parameters, decay heat is also known to be a significant uncertain parameter

that affects coolant boildown rates and uncovered fuel/cladding heatup rates. This uncertainty

was also included where Figure 3.1 shows the decay heat time history curves that were

developed from a series of SCALE6 [19] analyses informed by ANS-5.1 [24] time-dependent

decay heat uncertainties. The details of how the decay heat time history curves were developed is

discussed in Section 2.2.2 of [23] and in [15]. Out of the 30,000 decay heat time history curves

created a set of size 100 was randomly selected.

The “time-at-temperature” fuel rod failure treatment is a fairly recent modeling best practice

developed in the SOARCA analyses to address cliff-edge behaviors in fuel rod collapse that were

formerly treated as temperature threshold criteria. The time at temperature treatment, similar to a

Larsen miller or other lifetime model approaches introduces a smoothness to rod failure

prediction that mitigates undesired bifurcation tendencies of a simple failure threshold failure

approach. Simply stated, fuel that experiences a lower time-temperature integral will collapse at

a later time that fuel that experiences a larger time-temperature integral, ensuring that eventually

either will fail in time and unphysical bifurcations where one zone might fail and another never

fail are avoided. The methodology used to characterized time-at-temperature uncertainty is

described in Section 2.2.3 of [23]. The mean, median, 10th

percentile, and 90th

percentile time-at-

temperature curves, along with the time-at-temperature curve from the SOARCA analysis [8] are

shown in Figure 3.2. Out of the 27725 time-at-temperature curves created as set of size 100 was

randomly selected.

18

Table 3.1 – State of Knowledge Uncertain Parameters

Parameter Nomenclature

Shifted Beta

Distribution

(Mode/Mean)

time constants for radial (solid) debris relocation SC1020_1

LB = 180 s

UB = 720 s

α = 1.33

β = 1.67

time constants for radial (liquid) debris relocation SC1020_2

LB = 30 s

UB = 120 s

α = 1.33

β = 1.67

dT/dz model1, time constant for averaging hydrodynamic material

flows SC1030_2

LB = 0.09 s

UB = 0.11 s

α = 1.1

β = 1.1

dT/dz model, characteristic time for coupling dT/dz temperatures to

average CVH volume temperature when dT/dz model is active SC1030_4

LB = 8 s

UB = 12 s

α = 1.1

β = 1.1

dT/dz model, maximum relative weight of old flow in smoothing

algorithm involving time constant for averaging flows SC1030_5

LB = 0.5 s

UB = 0.7 s

α = 1.1

β = 1.1

molten zircaloy melt break-through temperature SC1131_2

LB = 2100 K

UB = 2540 K

α = 2.77

β = 2.33

molten cladding (pool) drainage rate SC1141_2

LB = 0.1 kg/m-s

UB = 2.0 kg/m-s

α = 1.11111

β = 1.8889

fraction of strain at which lower head failure occurs SC1601_4

LB = 0.16

UB = 0.20

α = 1.1

β = 1.1

scaling factor for candling heat transfer coefficients

cor_cht_hfzrXX

LB = 0.9

UB = 1.1

α = 1.1

β = 1.1

1 The dT/dz model is a subgrid model used in MELCOR to estimate axial temperature gradients in hydrodynamic

flow (e.g. steam) to account for gas heatup over the axial extent of an otherwise lumped temperature predicted for

the control volume. The purpose of the dT/dz model is to improve estimated for heat transfer from rods to flowing

gas within a given control volume.

19

Table 3.1– State of Knowledge Uncertain Parameters , Cont.

Parameter Nomenclature

Shifted Beta

Distribution

(Mode/Mean)

fraction of un-oxidized cladding thickness at which thermal-

mechanical weakening of oxidized cladding begins cor_rod_2

LB = 0.0005 m

UB = 0.0015 m

α = 1.1

β = 1.1

debris quenching heat transfer coefficient to pool cor_lp_2

LB = 100 W/m2 K

UB = 2000 W/m2 K

α = 1.1

β = 1.1

debris falling velocity cor_lp_4

LB = 0.01 m/s

UB = 1.0 m/s

α = 0.85

β = 1.14

minimum debris porosity (Lipinski dryout model); SC1244(1)

min. porosity used in flow blockage Ergun pressure drop

equation; SC4413(5)

min. hydrodynamic volume fraction; SC4414(1)

minimum porosity to be used in calculating the flow

resistance in the flow blockage model; SC1505(1)

minimum porosity to be used in calculating the area for heat

transfer to fluid; SC1505(2)2

minpordp

LB = 0.01

UB = 0.2

α = 1.1

β = 1.1

Table 3.1– State of Knowledge Uncertain Parameters , Cont.

Parameter Nomenclature Uniform

Distribution

Shifted Beta

Distribution

(Mode/Mean)

Fuel rod time-at-temperature relationship3 TaT n/a n/a

Time dependent core decay heat4 dch n/a n/a

2 Minimum debris porosity is defined in many locations of the MELCOR input. For consistency, the same

distribution and subsequent sampled values are applied to each sensitivity coefficient.

3 Fuel rod time-at-temperature and Time dependent decay heat are tabular uncertain variables, not point estimates.

See the appropriate section for more information regarding the characterization of these variables.

4 Fuel rod time-at-temperature and Time dependent decay heat are tabular uncertain variables, not point estimates.

See the appropriate section for more information regarding the characterization of these variables.

20

Figure 3.1 – Fukushima 1F1 Decay Heat Time History Curves

Figure 3.2 – Statistical Representation of Shark-Fin Failure Curves Overlaid on the SOARCA [4] Time at Temperature Failure Curve

21

3.2 UA Execution Figure 3.3 shows elements and processes used to perform the MELCOR calculations for the UA.

The 1F1 MELCOR input deck and the uncertain parameters are combined into the input

template for the MELCOR Uncertainty Engine software.

The MELCOR Uncertainty Engine software is run, producing “N” MELCOR input files

(where “N” is the number of realizations; for these analyses N = 100).

Pre-sampled files of Decay Heat tables and Time-at-Temperature tables are added to the

MELCOR input file set.

Each realization is run using MELGEN/MELCOR. The runs were executed on an SNL

Windows server cluster; a set of MELCOR output files is generated by each realization’s

execution.

Figure 3.3 – Diagram of Information Flow of the 1F1 Uncertainty Analysis

23

4 RESULTS OF MELCOR UNCERTAINTY ANALYSIS

A statistical analysis of these uncertainty analysis cases and other “perturbation case” results is

documented and discussed in reference [23] which describes more on methodology, statistical

importance of selected uncertain parameters and tests of statistical convergence. The remainder

of this report describes the uncertainty ranges of the selected fissured of merit and discusses their

significance to severe accident modeling and to anticipated Fukushima reactor decommissioning

activities.

4.1 Overview and General Observations Horsetail plots

5 with the statistical median (red) and median-like realization (rlz13) (green) are

given for the following figures-of-merit:

4.1.1 Primary System Pressure Response Shown in Figure 4.1 is the spectrum of predicted RPV pressure histories for the 100 realizations

examined in this study. Initially, the RPV pressure shows a large drop due to the delay between

scram and closure of the MSIVs. It then undergoes a series of increases when the ICs are not

running and decreases during IC operation. During this time the individual realization pressure

plots all nearly converge to the median value. This shows that the uncertainty (as characterized in

this analysis) in decay heat (the only uncertain parameter that is in effect over this time period)

has little to no effect on the RPV pressure during this time period.

Once the ICs are no longer operated (0.8 hr) due to loss of function on arrival of the tsunami, the

cycling of a single, low set-point SRV causes RPV pressure to oscillate between the SRV open

and close values between about 7.3 and 7.7 MPa.

The sudden drop in pressure predicted for all realizations between about 4 and 5.2 hours is due to

a predicted rupture of the main steam line associated with high core exit gas temperature

elevated RPV pressure (i.e., a Laron Miller creep rupture). Also, RPV water level begins to

decrease as water inventory is vented from the RPV to the WW by the SRV. During this time-

period the SRV open and close times begin to vary, which causes the RPV pressure to vary

between realizations.

5 A horsetail plot is a time vs. dependent parameter plot of all of the realizations for a given case (i.e., a 100

realization plot of total hydrogen generation would have 100 time-history curves. Typically the individual

realizations are plotted as thin, light grey lines, while statistical measures (e.g., median, mean, percentiles) are

overlaid with thicker, colored lines.

24

Figure 4.1. Primary system pressure showing range of predicted main steam failure times.

4.1.2 Core Water Level Boildown The predicted reactor vessel water level for each realization is shown in Figure 4.2. From this

figure we can see that predicted core uncovery times fall in a narrow band between about 2.2 and

2.4 hours. The water loss from the RPV during this time and up to about 4 hours is due to the

periodic cycling of the SRV’s and resulting venting of coolant inventory into the wetwell

suppression pool. Based on emerging information from ongoing Fukushima analyses in the

OECD BSAF program, it is further suspected that an additional RPV water loss not modeled in

these analyses may have occurred from leaking recirculation pump seals, which if modeled,

would advance the timing of core uncovering by some amount of time and accelerate other core

degradation events. This unmodeled leakage of water inventory would result in a direct steam

source to the drywell volume, bypassing the suppression pool and producing increased drywell

temperatures and pressures during the first 5 to 8 hours of the accident. This previously

unrecognized leakage of steam to the drywell will be included in future Fukushima sequence

examinations.

25

Figure 4.2. Reactor Vessel water level.

The water level loss rate shows a decrease rate between 3 and 4 hours as the water level reaches

the bottom of the core. This is due to decreased steam generation as less fuel “sees” liquid water

and a greater fraction of decay heat is retained in the fuel rods caused by the gradual diminished

steam cooling. Of course, as less heat is removed by steam cooling, more heat is retained in the

fuel rods causing heatup, onset of Zr oxidation and hydrogen generation and the beginning of

core degradation as the melting temperatures of the control blades and fuel rod cladding are

progressively exceeded. Between 3.5 and 4 hours the core water level begins to sharply decrease

due to the initial slumping of control blade materials and melted cladding Zr beginning to drop

(drain) from the upper core to the lower core and core plate regions, quickly boiling down the

residual water level in the core region.

During this time period core temperatures are rising rapidly, producing large amounts of

hydrogen from Zr oxidation (discussed later) from the steam that is now being produced by the

relocation of hot core materials into the lower core and lower plenum. This in turn produces high

core exit gas temperatures that vent to the suppression pool through the cycling SRV. These hot

gases flowing through the steam line of the cycling SRV are predicted to cause rupture of the

steam line in the upper containment region and a sudden decompression of the RPV. Associated

with this sudden drop in RPV pressure is the flashing of water in the RPV producing the sudden

drop in water level in this time period as discussed earlier.

In the time frame between 4.5 and 6.5 hours, the RPV water level progressively decreases due to

the boiling caused by the control blade and fuel materials that have gradually relocated to the

Steam flashing on failure of main steam

line and sudden RPV

decompression

Increased water loss

from relocating

control blade materials

Continued water loss

from relocating

control blade and fuel materials

Rapid water loss from larger fuel

relocations to lower head

Decreased water loss

(steam generation) due to low

water level in core

26

lower plenum. However, between about 6.5 hours and 8.5 hours a second large and sudden loss

of water level is observed, this time due to the larger collapse and relocation of larger quantities

of fuel components of the core into the lower plenum. The relocation of fuel bearing materials to

the lower head causes the complete dryout of the lower head and starts the “clock” for lower

head heatup and ultimate failure. Note however that lower head failure is not predicted for

several more hours following head dryout as will become apparent from subsequent figures

presented in this discussion.

4.1.3 Core Degradation and Relocation Events The core relocation events that drive the RPV pressure response and loss of water from the RPV

are described in the following discussions. The two major phases of core relocation identified in

the preceding, namely the initial relocation of lower melting point control blade materials

followed later by the relocation of higher melting point fuel materials is shown in Figure 4.3 and

Figure 4.4. These two major relocation events each cause significant steaming events as

relocating materials come to rest in the lower core and lower plenum regions, and these steaming

events in turn cause oxidation-driven temperature excursions and hydrogen generation. Note that

between 6 and 8.5 hours, all remaining water in the lower head has been boiled away even as

degrading core fuel bearing materials continue to accumulate in the lower plenum.

Close examination of Figure 4.3 and Figure 4.4 shows that the melting and relocation of control

blade materials precedes the failure and relocation of the fuel materials, but that there is also an

overlap of these processes for a period of time. (Keep in mind that these results are statistical

characterizations of the population and not a characterization of any particular realization.)

Eventually, the relocation of control blade materials from the core region to the lower plenum

becomes essentially complete, while gradual relocation of fuel materials to the lower plenum

persists for a longer period of time. Many realizations suggest that some residual fuel assemblies

can remain intact. These remaining intact assemblies are found to be located on the periphery of

the core where radial power profiles are low. This finding will be discussed later in the section

on “Fukushima Decommissioning Insights.” So a general observation is that initially low melting

point control blade materials relocate downward, followed by a period of both control blade and

fuel materials are relocating downward, and ending with a period where mainly fuel bearing

materials drop to the lower plenum. This may result in a layering of material compositions

initially accumulating on the lower head.

4.1.4 Hydrogen Generation Hydrogen generation from the oxidation of Zr and stainless steel core components with steam is

strongly dependent on temperature of the oxidizing materials and the availability of steam to

support the reaction. Oxidation, hydrogen generation and rapidly escalating temperatures

proceed concurrently until melting and relocation to lower cooler regions of the core takes place

which has the effect of terminating hydrogen generation and peak temperatures locally.

Hydrogen generation for the ensemble of realizations examined in this study is shown in Figure

4.5.

Hydrogen generation commences almost simultaneous with the first control blade relocations at

about 3.5 hours, driven by sufficiently high temperatures (exceeding 1500K) and enhanced steam

generation (control blade relocation to water in lower core and lower plenum). The two

processes have an autocatalytic feedback effect – escalating temperature drives blade relocation,

blade relocation drives steam generation, and steam generation drives oxidation and temperature

escalation and increased blade relocation.

27

The escalating temperatures driven by the oxidation transient, fortified by increased steaming

from blade relocation, produces high gas temperatures flowing through the steam line of the

lowest pressure setpoint SRV, as discussed earlier, and this in turn precipitates rupture of the

steam line due to high temperature and high pressure. The sudden depressurization flashes the

RPV water to steam, which temporarily cools the core region and ultimately reduces steam

production as the water level falls well below BAF. The result of this event is to temporarily

suppress hydrogen generation as seen in Figure 4.5 in the time range between 4 and 5.5 hours.

Hydrogen generation resumes however between 5.5 hours and 8 hours as fuel bearing materials

relocate to the water-filled lower plenum (Figure 4.4) when steam generation invigorates

oxidation of hot Zr-components still resident in the core region. Eventually, as core materials

continue to relocate to the lower plenum and the water inventory in the RPV is depleted, the

hydrogen generation again levels off. The range of maximum hydrogen generation at the end of

this sequence of events is predicted to be between about 675 and 900 kg.

Figure 4.3. Fraction of intact control rod mass in the core.

Time span of control blade relocations

28

Figure 4.4. Fraction of intact fuel mass in the core.

Figure 4.5. Mass of hydrogen produced in-vessel.

Main steam line failure time range

Time span of fuel material relocations to lower plenum

water

29

4.1.5 Lower Head Failure and Core Material Relocation to Reactor Cavity As seen in Figure 4.2 the reactor lower head water dryout is predicted to take place between 6.5

and 8.5 hours, after which steady heatup of the lower core debris accumulations transfer heat to

the RPV lower head. More than half of the realizations did not predict lower head failure by 15

hours when emergency cooling water was injected by core spray line. Had cooling water not

been injected, these cases would have eventually led to head failure. For those cases that did

result in lower head failure, it was from loss of strength as the inner vessel wall material melts

under the heat load from the core debris. The predicted mass released to the cavity region is

shown in Figure 4.6. For the most part, on failure of the RPV lower head, most accumulated

materials are released to the reactor cavity within 10 or 15 minutes; however in some realizations

the transfer of materials proceeds over a protracted period, perhaps an hour or so. Most

realizations relocate a large fraction of the total core inventory, suggesting a generally large

degree of core failure and ultimate transfer to the cavity region. However, as discussed earlier,

some fraction of realizations suggest that not all regions of the core are failed and that some

residual core materials can be expected in the peripheral regions of the core. This will be

discussed further in the following section on decommissioning implications.

Figure 4.6. Mass of core material ejected to the reactor cavity.

4.1.6 Containment Pressure Response The containment drywell and wetwell pressures, shown in Figure 4.7 and Figure 4.8 respectively

show the response to the SRV venting steam to the suppression pool as well as the RPV

depressurization event. The SRV venting period prior to the main steam line rupture brings about

a gradual increase in wetwell/drywell pressure as the water in the suppression pool increases in

temperature gradually increasing the saturation pressure of the wetwell and the entire

containment. This gradual increase in drywell and wetwell pressure during the SRV cycling

30

period appears to produce correspondingly higher containment peak pressures at the timing of

main steam line rupture, presumably due to diminished heat sink available in the wetwell at the

actual time of RPV blowdown. Subsequent to the MSL rupture time, containment pressures

gradually increase in time owing to increased accumulation of decay heat in the containment

(recall the containments are isolated from the ultimate heat sing of the ocean). Sporadic sharp

increases in containment pressure are driven by melt relocation events from the core to the lower

plenum of the vessel during the time frame of 5.5 to 7.5 hours, where intense steam generation in

the lower plenum translates to corresponding increases in containment pressure.

The late-time “step” increases in containment pressures in the 12 to 15 hour time period are

associated with lower head failure when core debris falls to the reactor cavity floor producing

additional steam generation from debris contact with water present in the cavity region from the

earlier MSL rupture, and a corresponding containment pressure response.

The DW and WW pressures tend to track each other over the duration of the simulation, as they

must since they are closely coupled aside from a small hydrostatic head difference associated

with the submergence of the drywell vents below the water level in the wetwell suppression pool.

As discussed earlier, significant increases in both the DW and WW pressures begin coincident

with the start of fuel failure; this is due to material relocation into the remaining water in the

core, resulting in the production of steam.

Figure 4.7. Containment drywell pressure response.

MSL Failure

Melt relocations in-vessel

Lower head failure

31

Figure 4.8. Containment wetwell pressure response

4.2 Observations and Areas of Future Improvement Direct comparisons to Fukushima data have not been made in this study, and while the prediction

of these analyses are generally consistent with the available accident response data, some

differences remain where we have good insights into future improvements in these analyses.

Examples include the modeling of a direct steam leakage to the drywell from leaking

recirculation pump seals, and recognition of the need to increase in drywell containment

nodalization (to capture local heating caused by MSL rupture), both aimed at improving our

comparison to measured containment pressure response. The timing of core damage events is

generally consistent with known accident progression trends. In this study, lower head failure is

judged to be a bit too late compared to Fukushima data trends, mainly the drywell pressure

response and radiation detection at the Fukushima main gate, which strongly suggest lower head

failure at around 12 hours in comparison to the 13 to 15 hour range inferred by these analyses –

in fact most realizations (about 2/3’s) did not result in lower head failure by 15 hours.

Improvement in these accident signatures is expected base on new and emerging understanding

of these accidents. However, the general trends concerning degree of damage to core region,

retention of core materials in the RPV, and the ultimate mass of materials transferred to the

reactor cavity region are considered reasonable and informative for planning of

decommissioning activities.

The following section addresses specific decommissioning issues that can be informed by these

uncertainty analyses.

MSL Failure

Melt relocations in-vessel

Lower head failure

33

5 IMPLICATIONS FOR FUKUSHIMA DECOMMISSIONING

The Nuclear Damage Compensation and Decommissioning Facilitation Corporation (NDF) has

been established in Japan in part to facilitate and coordinate development of decommissioning

technologies that will be needed in order to defuel and decommission the damaged reactors at the

Fukushima Daiichi Nuclear Power Station. NDF has sought insights from severe accident code

analyses performed under the OECD-NEA BSAF6 project. The selected figures of merit

examined in this uncertainty study were aimed at providing such key insights to the

decommissioning activities. In order to design defueling equipment and strategies it will be most

helpful to understand the degree of damage in the reactor cores, the expected final distribution of

core materials within the original core region, fuel debris remaining in the vessel lower head

region and expected amounts of material that exited the reactor pressure vessel and fallen to the

reactor pedestal/cavity floor.

This uncertainty study explores those damage characteristics and presents potential ranges or

distributions for those characteristics to inform the decommissioning activities about the range

and likelihood of expected damage configurations and thereby provide the information useful to

specifying design requirements for decommissioning equipment and strategies. The figures of

merit explored in this study are the principal obvious quantities of first order importance in

decommissioning actions; however, future analyses could be performed to inform the expected

conditions in the Units 2 and 3 Fukushima Daiichi reactors and additional figures of merit

defined to address more specific questions and issues as decommissioning activities move

forward. Uncertainty analyses provide a far broader spectrum of information than is possible

from single deterministic analyses. The following sections review those key figures of merit and

provide additional interpretation on decommissioning implications.

5.1 Relocation of Control Blades and Fuel Bearing Materials to Lower Vessel Head

Because of the lower effective melting temperature of the stainless steel/B4C control blades

(about 1500K owing to boron-iron eutectic formation), the control blade components are the first

major core components to melt and drain from the core region. Figure 4.3 showed the “horsetail

plot” results for all uncertainty realizations as a function of time. In a sense, the horsetails plots

provide graphical visualization of the time-varying probability density function. The uncertainty

information may be presented in a slightly different manner by forming approximations for the

cumulative distribution function at selected times by arranging the values at any point in time in

rank order and plotting the cumulative fraction of the values that are less than the specified

ordinate value. The control blade fraction remaining in the core is shown in this way in Figure

5.1 for times of 4.8, 5.8, 10 and 15 hours. (These selected times are somewhat arbitrary but were

chosen to coincide with observed events in the control blade and fuel material failures.) From

this figure we can see that at 4.8 hours essentially all realizations exhibited no more than about

58% of control blade material remaining in the core region and no realizations exhibited less

than about 27% of blade material. Put another way, all cases by 4.8 hours had relocated between

6 NEA Benchmark Study of the Accident at the Fukushima Daiichi Nuclear Power Station (BSAF) Project – this

international project is focused on forensics evaluation of the accidents at Fukushima using severe accident

analysis codes such as MELCOR. (https://www.oecd-nea.org/jointproj/bsaf.html)

https://www.oecd-nea.org/jointproj/bsaf.html

34

40% and 73% of the control blade materials to the core plate or lower plenum. By 5.8 hours, all

cases relocated at least 60% of control blade materials to the lower core or lower plenum region

and about 10% of the cases had relocated between 80 and 100 percent of control blade material

to lower core plate or below. By 10 hours, 60% of cases have relocated all control materials to

lower core plate or below, about 35% have relocated 95% of control blade material and only

about 5% of cases have relocated less than 90% of control blade materials. By 15 hours, all

realizations have relocated all in-core control materials to the lower core plate and below.

Figure 5.1. Fraction of Intact Control Blade Mass in Core Region.

The undamaged fuel materials remaining in the core region shown in Figure 5.2 follows a similar

trend except lagging in time behind the control blade relocations and some cases suggest a

residual amount on undamaged fuel remaining in the core region by 15 hours. By the end of the

core damage phase, about 2 percent of the cases show up to 30% of the fuel remaining in the

core while 8 % or the realizations predicted a few percent of the fuel assemblies to be remaining

undamaged in the core region.

Ignoring spatial incoherencies across the core, these trends suggest a layering of materials may

have accumulated in the lower reactor head, where early in time, this lower-most layer is

comprised of mainly control blade materials (from 50% to 70% of the total amount of control

blade materials), namely stainless steel and boron carbide with very little fuel bearing materials.

For a while both control blade materials and fuel bearing materials are relocating downward at

the same time, but perhaps not coherently from a spatial point of view. These accumulations may

be comprised of comparable fractions of core totals of control blade and fuel bearing materials.

And towards the end of the core slumping period, mostly fuel bearing materials are relocating

downward on top of the previously accumulated debris.

35

Figure 5.2. Fraction of Intact Fuel Mass in Core Region.

5.2 Relocation of Lower Head Materials into Reactor Cavity Shown in Figure 5.3 is the CDF for the mass of core materials ejected to the cavity following

failure of the vessel lower head. Note that in this case not all realizations actually resulted in

lower head failure by 15 hours, the time at which water injection was restored to the Unit 1

reactor. This reveals both a bias in the MELCOR predicted head failure time, as the Fukushima

Daiichi Unit 2 vessel head is widely thought to have failed at about 12 hours, whereas the central

tendency of the realizations for this study was centered more in the 13 to 15 hour time frame.

Causes for this apparent bias will be investigated further in future studies, but the finding also

suggests that the Unit 1 reactor damage progression perhaps had some chance of being arrested

had the water injection been accomplished a few hours sooner.

The CDF shown in Figure 5.3 was formed conditional on the lower head having failed before the

15 hour time of water injection. The realizations shown in the horsetail plots of Figure 4.6 and

the conditional CDF in Figure 5.3, reveal that in most cases head failure results in the rapid

discharge of most of the accumulated debris of the lower vessel. About 60% of the realizations

with lower head failure resulted in the discharge of 95 to 100% of the core material accumulated

in the lower head. About 30% of the cases ejected between 80 to 95%, while about 5% of the

cases showed ejection of only about 30% of the lower head debris accumulations.

36

Figure 5.3. Mass of Core Materials Ejected to Reactor Cavity.

5.3 Conclusions of Material Relocation Analysis These results suggest that most fuel bearing and control blade materials are expected to have

relocated from the core region to the lower vessel head and that a layering of accumulations

might have been formed by the relocations with the lower-most layer comprised of about half of

the control blade stainless steel and boron carbide. Subsequent layers of debris with mixtures of

fuel bearing debris and control blade material are suggested with the final core material falling to

the lower head being largely comprised of fuel bearing materials. Not investigate in this study is

the ultimate fate of the significant lower plenum stainless steel control rod guide tubes which

would have been subsumed into this layered accumulation of core materials. This possibility

could be further investigated in a future study. In that compounds that include boron carbide can

be among the hardest materials known to exist, some of these aggregations implied in these

observations could prove quite challenging to the defueling process. This study implies that the

decommissioning activities can expect to encounter some residual fuel assemblies remaining in

the core regions, likely on the periphery of the core, perhaps 15 to 20% of the lower head debris

accumulations retained in the head regions (or on the drive tubes that reside below the vessel

head – not currently modeled in MELCOR), and as much as 80 to 100 % of the core materials

relocated to the cavity region. Because of the layering suggested by the time-phased relocation of

control blade and fuel bearing materials, the debris accumulations may not be homogeneous in

nature, something to bear in mind when designing defueling equipment.

37

5.4 Hydrogen Generation For completeness, cumulative distribution functions were estimated from the hydrogen horsetail

results and are shown in Figure 5.4. The spread in the predicted hydrogen generation shows a

similar dispersion around the median values for the 4 time periods selected. As time progresses,

the total amount of hydrogen steadily increases and the upper end of the hydrogen predicted at

5.8 hours shows a greater dispersion than the lower end of the CDF. By 10 hours, most of the

hydrogen that could be produced has been realized and only a little more hydrogen is seem by

the end of the sequence analyses at 15 hours. This is both due to consumption of available

metallic zirconium by oxidation, its relocation to cooler regions of the reactor vessel, as well as

by the termination of steam generation from boildown and water loss. Such characterization of

hydrogen generation can facilitate design and placement of hydrogen igniters and recombiners

by providing information on the maximum quantities that can be realized and the timing of

hydrogen generation. While informative on hydrogen generation and its uncertainty, these results

do not bear strongly on decommissioning issues other than to indicate the possible maximum

quantities of oxidized cladding and channel box materials that will be encountered in the

defueling activities.

Figure 5.4. Hydrogen Generation Results shown in CDF format.

39

6 A QUANTIFICATION OF CODE NUMERICAL SENSITIVITY WITH IMLICATIONS ON PRECISION OF COMPUTED RESULTS

In order to assess MELCOR sensitivity to the parameters varied in this uncertainty study and to

quantify known code numerical inherent noise levels in response to response to changes in code

input values, a special case was examined where the same uncertainty parameters were

considered except that their sampled distributions were narrowed considerably such that only

small changes in each sampled parameter around a central value were sampled. A mean-like

member of the main study (realization number 13) was used so that the central tendency of the

narrow distribution sensitivity study should closely resemble the central tendency of the main

uncertainty study. The narrowed distribution parameters are summarized in Table 6.1 where

about 1% variation around the central value was allowed in the sampling process.

The results of the small perturbation variations are shown to the right of the base case results of

the uncertainty distribution in Figure 6.1 through Figure 6.8 to highlight the degree of numerical

noise that results from small changes in code input values. Some general observations can be

made from these comparisons. Firstly, the central values of the small perturbation realizations

are largely consistent with the central tendencies of the full uncertainty study results. This

suggests that code accuracy is maintained while varying the inputs over a small range.7 The

small perturbation results however show the inherent limitations on code precision where some

degree of scatter in results about a central tendency remains as parameter range variations are

reduced to small values. Secondly, the variation in results from the full uncertainty analysis

covers a larger span than does the small perturbation analyses. This implies that we can tell the

difference between true uncertainty variance and the underlying numerical variations even

though the residual numerical variation is on the same order of magnitude as our uncertainty

results. Thus these results both qualitatively quantify our predicted uncertainty ranges due to

state of knowledge uncertainty as well as our code model precision level.

Examination of Figure 6.4 and Figure 6.5 suggest that material relocations events may be

important contributors to the scatter in subsequent code results possibly due to the large impacts

on the solution matrix as sudden large movements of core materials take place. This will be a

subject of further investigation in future studies.

7 Here accuracy implies that the central tendency of the code scatter in results is maintained. That is to say that the

code produces consistent results on average for the given input values. Precision on the other hand is used to

describe the size of the scatter around the central tendency.

40

Table 6.1 – Small Change Distributions Based on Rlz13 Sampled Values.

parameter nomenclature uniform distribution

time constants for radial (solid) debris relocation SC1020_1

L.B. = 4.3004E+02 Rlz13 = 4.3220E+02 U.B. = 4.3436E+02

time constants for radial (liquid) debris relocation SC1020_2

L.B. = 6.5580E+01 Rlz13 = 6.5910E+01 6.6240E+01

dT/dz model, time constant for averaging flows SC1030_2

L.B. = 9.1232E-02 Rlz13 = 9.1690E-02 U.B. = 9.2148E-02

dT/dz model, characteristic time for coupling dT/dz

temperatures to average CVH volume temperature when

dT/dz model is active SC1030_4

L.B. = 8.8286E+00 Rlz13 = 8.8730E+00 U.B. = 8.9174E+00

dT/dz model, maximum relative weight of old flow in

smoothing algorithm involving time constant for

averaging flows SC1030_5

L.B. = 5.6377E-01 Rlz13 = 5.6660E-01 U.B. = 5.6943E-01

molten zircaloy melt break-through temperature SC1131_2

L.B. = 2.3492E+03 Rlz13 = 2.3610E+03 U.B. = 2.3728E+03

molten cladding (pool) drainage rate SC1141_2

L.B. = 3.4367E-01 Rlz13 = 3.4540E-01 U.B. = 3.4713E-01

fraction of strain at which lower head failure occurs SC1601_4

L.B. = 1.7313E-01

Rlz13 = 1.7400E-01

U.B. = 1.7487E-01

scaling factor for candling heat transfer coefficients cor_cht_hfzrXX

L.B. = 1.0826E+00

Rlz13 = 1.0880E+00

U.B. = 1.0934E+00

fraction of un-oxidized cladding thickness at which

thermal-mechanical weakening of oxidized cladding

begins

cor_rod_2

L.B. = 1.3094E-03

Rlz13 = 1.3160E-03

U.B. = 1.3226E-03

debris quenching heat transfer coefficient to pool cor_lp_2

L.B. = 9.8654E+02

Rlz13 = 9.9150E+02

U.B. = 9.9646E+02

debris falling velocity cor_lp_4

L.B. = 4.6894E-01

Rlz13 = 4.7130E-01

U.B. = 4.7366E-01

minimum debris porosity (Lipinski dryout model);

SC1244(1)

min. porosity used in flow blockage Ergun pressure drop

equation; SC4413(5)

min. hydrodynamic volume fraction; SC4414(1)

minimum porosity to be used in calculating the flow

resistance in the flow blockage model; SC1505(1)

minimum porosity to be used in calculating the area for

heat transfer to fluid; SC1505(2)

minpordp

L.B. = 8.9968E-02

Rlz13 = 9.0420E-02

U.B. = 9.0872E-02

41

Figure 6.1. Base Case Comparison with Small Perturbation analysis (RPV pressure).

Figure 6.2. Base Case Comparison with Small perturbation analysis (Drywell Pressure).

Figure 6.3. Base Case Comparison with Small Perturbation analysis (RPV Water Level).

42

Figure 6.4. Base Case Comparison with Small perturbation analysis Intact Control Rod Mass).

Figure 6.5. Base Case Comparison with Small perturbation analysis Intact Fuel Mass).

Figure 6.6. Base Case Comparison with Small perturbation analysis (In-Vessel Hydrogen).

43

Figure 6.7. Base Case Comparison with Small perturbation analysis (Mass of Water in the Lower Plenum).

Figure 6.8. Base Case Comparison with Small perturbation analysis (Mass of Material Ejected).

45

7 SUMMARY AND CONCLUSIONS

An uncertainty analysis was performed for the Fukushima Daiichi Unit-1 accident sequence

focused primarily on melt progression uncertainties as modeled in MELCOR. MELCOR input

parameters known to affect melt progression behavior were treated as uncertain where

distributions of parameter values were estimated to reflect the state of knowledge uncertainty for

these key code parameters. The parameters were sampled randomly to produce 100 MELCOR

realizations (predictions) reflecting this uncertainty in the range of their predicted variations. A

primary motivation for this study was to identify ranges of predicted core damage states in order

to inform future decommissioning activities for the damaged reactors at the Fukushima site. Key

figures of merit were identified as code output aimed at characterizing potential reactor damage

states such as state of damage to core control blades, fuel rods in the core, and their relocation

and redistribution throughout the core region, lower vessel region and reactor pedestal/cavity

region for cases where lower head failure was predicted within the first 15 hours of the accident.

Hydrogen generation during the core damage progression was also investigated.

From these results several key findings were observed. One of these findings is that the control

blade failure and relocation initiates prior to the first fuel damage events and results in the

stainless steel and boron carbide blade materials draining downward to the lower core plate and

vessel lower plenum. About half of the control blade materials have relocated downward before

the first fuel rod failures occur. Then for a while, both control blade and fuel rod failures occur

simultaneously, mainly due to temporal and spatial incoherencies across the core. Finally, there

is a period where only fuel rod failures produce downward relocation events. We suggest that

this could produce a layering of materials with lower melting point control blades are rich in the

lower accumulations in the vessel lower head, with mixtures of control blade and fuel material,

followed last by an upper layer of fuel rich accumulations.

The results also suggest that some relatively undamaged fuel assemblies could still be in the core

regions around the periphery of the core and that some residual debris of control blade and fuel

material may also have been retained in the vessel lower head. Most cases that predicted lower

head failure before 15 hours suggested a rapid and nearly complete relocation to the reactor

cavity region (recalling that some cases showed residual material retained in the vessel lower

head). These general findings can inform anticipated Fukushima decommissioning activities by

identifying the regions both in-vessel and ex-vessel where fuel materials are likely to be found.

Not addressed in this study are potentially significant implications concerning high temperature

damage to the vessel upper internal components, chiefly steam separators and dryers. Damage to

these components from high core exit gas temperatures could significantly complicate their

removal in the decommissioning phase. Future studies could address logistical issues such as

this.

The study was scoping in nature and several areas of potential improvement were identified to

improve accident signatures such as better prediction of lower head failure timing and improved

comparison to containment pressure data. Such refinements will improve the accuracy of the

predicted figures of merit and provide better guidance to decommissioning activities.

Characterizing the effect of uncertainty on the anticipated core and vessel damage states helps

ensure that a more complete range of potential damage states are identified.

46

Results presented in this study considers effects of the codes numerical precision through a

comparison of sampled results from narrow-range uncertainty input uncertainty distributions.

This was done to quantify the order of magnitude of the numerical precision that can be expected

of complex multi-effects codes, in this case MELCOR, in the midst of broader state of

knowledge variations employed in the uncertainty analysis. It was shown that this presently

irreducible variability is smaller than the variation of results produced from sampling the full

range of uncertain inputs, although of the same order of magnitude. It was also shown in the

reduced range sampling study that the central tendency of the sampled results was not shifted

from the full uncertainty range sampling results. This means that the numerical imprecision,

while adding to the complexity of quantifying the sensitivity of the uncertainty in the input to the

variability in the output, must be considered when assessing the magnitude of variance in the

results.

The utility of this methodology in identifying a broad range of plausible damage states for

assisting in decommissioning planning has been demonstrated and areas of improvement

identified. The methodology could be readily extended to the other Fukushima Daiichi Unit 2

and Unit 3 accident sequences and thereby similarly inform decommissioning activities for these

reactors as well.

47

8 REFERENCES

1. R.O. Gauntt, et al., NUREG/CR-6119, "MELCOR Computer Code Manuals, Vol. 2:

Reference Manuals, Version 1.8.6 (Vol. 2, Rev. 3)," Sandia National Laboratories,

Albuquerque, NM, 2005.

2. R.O. Gauntt, et al., SAND2012-6173, "Fukushima Daiichi Accident Study (Status as of

April 2012)," SNL, Albuquerque, NM, 2012

3. U.S. Nuclear Regulatory Commission, State-of-the-Art Reactor Consequence Analyses

(SOARCA) Report, NUREG-1935,Washington, DC, 2012.

4. Sandia National Laboratories, NUREG/CR-7155, “State-of-the-Art Reactor Consequence

Analyses Project: Uncertainty Analysis of the Unmitigated Long-Term Station Blackout

of the Peach Bottom Atomic Power Station – DRAFT Report,” USNRC, Washington,

DC, 2013. 2012.

5. State-of-the-Art Reactor Consequence Analyses Project Uncertainty Analysis of the

Unmitigated Short-Term Station Blackout of the Surry Power Station” DRAFT Report.

6. M. Pellegrini, “Benchmark Study of the Accident at the Fukushima Daiichi Nuclear

Power Plant, Phase I Report”, The Institute of Applied Energy, Nuclear Power

Engineering Center, Tokyo, Japan. 2015.

7. D. Luxat, D. Kalinich, J. Hanophy, 3002004449, “Modular Accident Analysis Program

(MAAP) – MELCOR Crosswalk, Phase 1 Study,” EPRI, Palo Alto, CA, 2014.

8. U.S. NRC. “State-of-the-Art Reactor Consequence Analysis Project, Volume 1: Peach

Bottom Integrated Analysis,” NUREG/CR-7110, Volume 1, U.S. Nuclear Regulatory

Commission: Washington DC. 2012.

9. M.T. Leonard, R.C. Nause, R.O. Gauntt. “A General Purpose MELCOR Model of a

BWR/4 Mark I Nuclear Power Plant,” DRAFT, Sandia National Laboratories,

Albuquerque, NM. 2008.

10. Distribution of 1F1-3 plant specifications”. Letter and enclosures from F. Nagase (JAEA)

to R.O. Gauntt (SNL), October 13, 2013.

11. J.J. Carbajo. “Severe Accident Source Term Characteristics for Selected Peach Bottom

Sequences Predicted by the MELCOR Code.” NUREG/CR-5942, Oak Ridge National

Laboratory: Oak Ridge, TN. July 1993.

12. D.A. Powers, et al. “Accident Source Terms for Light-Water Nuclear Power Plants Using

High-Burnup or MOX Fuel,” SAND2011-0128, Sandia National Laboratories:

Albuquerque, NM. January 2011.

13. L. Soffer, et al. “Accident Source Terms for Light-Water Nuclear Power Plants,”

NUREG-1465, U.S. Nuclear Regulatory Commission: Washington, DC. February, 1995.

14. K. Ross, et al., “MELCOR Best Practices as Applied in the State-of-the-Art Reactor

Consequence Analyses (SOARCA) Project,” NUREG/CR-7008, U.S. Nuclear

Regulatory Commission. Washington, DC. August, 2014.

48

15. J. Cardoni, “Radionuclide Inventory and Decay Heat Quantification Methodology for

Severe Accident Simulations,” SAND2014-17667, Sandia National Laboratories,

Albuquerque NM, 2014.

16. Private Communication from M.T. Leonard to D.A. Kalinich, June 2012.

17. OECD-NEA Benchmark Study of the Accident at the Fukushima Daiichi NPS Project

(BSAF Project), “Operational Data,” Tokyo, Japan. November 6-8, 2012.

18. U.S. NRC, “Severe Accident Risks: An Assessment for Five U.S. Nuclear Power Plants –

Final Summary Report”, NUREG 1150, Vol. 1, U.S. Nuclear Regulatory Commission,

Washington, D.C., 1990.

19. Oak Ridge National Laboratory, “Scale: A Comprehensive Modeling and Simulation

Suite for Nuclear Safety Analysis and Design,” ORNL/TM-2005/39, Version 6.1, June

2011. Available from Radiation Safety Information Computational Center at Oak Ridge

National Laboratory as CCC-785.SCALE reference

20. TEPCO Plant Data, http://www.tepco.co.jp/nu/fukushima-np/plant-

data/f1_3_Keihou3.pdf, accessed April 6 (2014).

21. Paul Bourke, http://paulbourke.net/fractals/

22. J. Cardoni and D. Kalinich, “Fukushima Daiichi Unit 1 Uncertainty Analysis –

Preliminary Selection of Uncertain Parameters and Analysis Methodology,”

SAND2014-1170, Sandia National Laboratories, Albuquerque, NM, 2014.

23. M.R. Denman and D.M. Brooks, “Fukushima Daiichi Unit 1 Uncertainty Analysis –

Exploration of Core Melt Progression Uncertain Parameters – Volume II,”

SAND2015-6612, Sandia National Laboratories, Albuquerque, NM, 2015.

24. American National Standard for Decay Heat Power in Light Water Reactors,

ANSI/ANS-5.1-2005, American Nuclear Society (2005).

http://www.tepco.co.jp/nu/fukushima-np/plant-data/f1_3_Keihou3.pdf

http://www.tepco.co.jp/nu/fukushima-np/plant-data/f1_3_Keihou3.pdf

http://paulbourke.net/fractals/

49

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